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Density and Viscosity of Orange Oil and Turpentine Biofuels šŸŒ± #WorldResearchAwards

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Introduction Biofuels are increasingly recognized as sustainable alternatives to fossil fuels due to their renewable origin, reduced greenhouse gas emissions , and compatibility with existing energy systems. Among emerging biofuel components, orange oil , turpentine , and their hydrogenated derivatives have gained attention because they are derived from biomass resources and industrial by-products. Understanding their physicochemical properties is essential for evaluating their feasibility in energy applications. This research focuses on generating reliable experimental data to support the potential integration of these essential oils into biofuel formulations. Experimental methodology and measurement accuracy Accurate determination of density and viscosity is critical for assessing fuel performance in internal combustion engines . In this study, densities and viscosities of orange oil, turpentine, hydrogenated orange oil , and hydrogenated turpentine were measured at atmospher...
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Practical test-time domain adaptation for industrial condition monitoring #worldresearchawards

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  Introduction Machine learning has become a cornerstone of modern industrial analytics , particularly in condition monitoring systems that rely on sensor data for fault detection and health assessment . Despite strong performance during development, many models suffer significant degradation when deployed in real-world environments due to domain shift , where operational conditions differ from training settings. This challenge motivates the need for adaptive, practical, and deployment-ready learning frameworks that can sustain reliability without continuous manual intervention. Domain shift challenges in industrial condition monitoring Industrial condition monitoring systems operate under highly dynamic environments involving varying loads, speeds, sensor types, hardware configurations, and environmental noise. These variations induce domain shifts that violate the assumptions made during model training, leading to increased false alarms or missed fault detections. Traditiona...
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Attention-guided multi-task learning for fault detection in power systems #worldresearchawards

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Introduction Timely and accurate fault diagnosis is a cornerstone of modern power transmission system operation, directly influencing grid stability , safety, and service continuity. With increasing system complexity and penetration of intelligent devices , conventional single-task diagnostic approaches often fall short in meeting real-time and accuracy requirements. This research addresses these challenges by proposing a unified deep learning framework that integrates fault identification, fault type classification, and fault location estimation into a single multi-task learning paradigm , tailored for realistic transmission network conditions . Multi-task learning framework for fault diagnosis The proposed framework adopts a multi-task learning (MTL) architecture that enables simultaneous learning of multiple fault-related objectives within a shared representation space. By leveraging common features across tasks, the model reduces redundancy and improves generalization compared...
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A Semi-Automatic Framework for Dry Beach Extraction Using Photogrammetry šŸŒšŸ† #WorldResearchAwards

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Introduction The spatial configuration of dry beaches in tailings ponds is a key indicator for assessing tailings dam safety and operational stability. Traditional two-dimensional image-based approaches often fail to capture the true geometric and spatial characteristics required for accurate monitoring. To address these limitations, this research integrates deep learning –based semantic segmentation with three-dimensional reconstruction from UAV imagery , enabling robust, high-precision dry beach extraction and analysis in complex tailings environments. UAV-based 3d reconstruction for tailings analysis High-resolution UAV images are used to reconstruct detailed 3D point clouds of tailings ponds, forming the geometric foundation of the proposed method. By deriving the projection matrix for each image, a precise correspondence between 2D image pixels and 3D spatial points is established. This step enables accurate spatial mapping and overcomes the inherent depth ambiguity of conv...
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Low-Temperature Īµ-Ga₂O₃ Films for Solar-Blind Detectors | #WorldResearchAwards #Ga2O3

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Introduction Īµ-Ga₂O₃, a metastable yet technologically important polymorph of the gallium oxide family , has recently gained significant attention for its potential in high-power electronics and solar-blind photodetectors . Its exceptional optoelectronic characteristics, wide bandgap , and inherent crystallographic advantages make it highly attractive for next-generation device architectures. However, synthesizing pure-phase Īµ-Ga₂O₃ through low-temperature and energy-efficient methods has long remained a major challenge due to its metastability and the tendency to convert into more stable phases such as Ī²-Ga₂O₃. The present research addresses this limitation by demonstrating the successful low-temperature fabrication of phase-pure, highly oriented Īµ-Ga₂O₃ thin films using thermal atomic layer deposition (ALD), opening new pathways for scalable and high-performance device applications. Low-Temperature Synthesis Strategy The study introduces a low-energy thermal ALD route utilizing trim...
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Industry Impact Award | Transforming Innovation & Global Excellence #WorldResearchAwards

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Introduction The Industry Impact Award stands as a premier recognition dedicated to honoring exceptional researchers, innovators, and professionals whose contributions have significantly advanced global industries. This award highlights individuals and collaborative teams whose work bridges scientific discovery and industrial application, ultimately driving progress in technology, sustainability, manufacturing, and societal development. By celebrating excellence, the initiative motivates the global research community to pursue groundbreaking ideas that shape the future of industrial transformation. Research-Driven Industrial Innovation Modern industrial advancement increasingly relies on research breakthroughs that redefine efficiency, productivity, and technological capabilities. Awardees in this category demonstrate exceptional skill in translating academic findings into real-world solutions that strengthen industrial systems. Their contributions often accelerate the evolution of s...
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Photonic-Assisted SBS Frequency & AoA Measurement | #WorldResearchAwards #Photonics

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Introduction The advancement of modern radar and electronic warfare systems increasingly depends on the ability to detect, analyze, and interpret microwave signals with high precision. Traditional electronic measurement techniques face limitations in bandwidth, real-time performance, and interference tolerance, prompting the exploration of photonic-based solutions . The discussed research introduces a novel scheme utilizing stimulated Brillouin scattering (SBS) for simultaneous detection of frequency and angle-of-arrival (AOA) of microwave signals. By converting spatial information into optical domain interference and mapping spectral features through frequency-to-time transformation , the architecture achieves multidimensional parameter extraction in real time. This work highlights the significance of photonic sensing as a pathway to high-speed, wide-band, and highly accurate microwave measurement technologies. Photonic-Based Multidimensional Microwave Sensing This study pro...
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